【摘要】：隨著網(wǎng)絡(luò)規(guī)模的不斷擴大,網(wǎng)絡(luò)中運行的網(wǎng)絡(luò)設(shè)備如路由器、交換機等設(shè)備日益增多,能夠確保網(wǎng)絡(luò)正常運行,維護網(wǎng)絡(luò)設(shè)備不出現(xiàn)故障,在出現(xiàn)故障之后能夠迅速、準(zhǔn)確地定位問題并排除故障,對于網(wǎng)絡(luò)維護和管理人員是個很大的挑戰(zhàn)。 為了克服傳統(tǒng)維修方式的不足,隨著狀態(tài)監(jiān)測和故障診斷技術(shù)的不斷進步,逐漸發(fā)展起來一種新的維修方式——基于狀態(tài)的維修(CBM)。該維修方式綜合運用各種技術(shù)手段來獲取設(shè)備的運行狀態(tài)數(shù)據(jù),然后運用故障預(yù)測和診斷技術(shù)對設(shè)備的運行狀態(tài)進行判別,并預(yù)測其發(fā)展趨勢以及診斷發(fā)生何種故障,實現(xiàn)了通過狀態(tài)監(jiān)測預(yù)測即將發(fā)生的故障,制訂合理的維修策略。故障預(yù)測技術(shù)是故障診斷技術(shù)的重要組成部分,是通過對歷史和當(dāng)前的故障特征值進行分析,預(yù)測出未來的故障特征值,從而預(yù)測出設(shè)備在未來一段時間內(nèi)的運行狀態(tài),預(yù)測設(shè)備可能出現(xiàn)的故障,并且依據(jù)這些特征值,判斷設(shè)備的故障級別,提前掌握設(shè)備故障的發(fā)展趨勢,為提早預(yù)防和修復(fù)故障提供依據(jù),具有重要的理論研究價值和工程實踐意義。 本文提出了基于神經(jīng)網(wǎng)絡(luò)的故障預(yù)測方法,引入基于狀態(tài)的維修技術(shù),構(gòu)建了基于多狀態(tài)在網(wǎng)運行設(shè)備故障預(yù)測模型。該模型根據(jù)故障的嚴(yán)重性將預(yù)警等級劃分為四層,對于不同的預(yù)警級別,分別構(gòu)建神經(jīng)網(wǎng)絡(luò),解決了設(shè)備故障預(yù)測精度不高的難題,提升了基于多狀態(tài)的故障預(yù)測能力。通過收集網(wǎng)絡(luò)設(shè)備運行特征信息,得到設(shè)備的特征信息樣本集,應(yīng)用設(shè)計完成的神經(jīng)網(wǎng)絡(luò)對樣本集進行訓(xùn)練,進一步優(yōu)化神經(jīng)網(wǎng)絡(luò)的設(shè)計結(jié)構(gòu),建立基于神經(jīng)網(wǎng)絡(luò)的故障預(yù)測模型,實現(xiàn)對設(shè)備故障的預(yù)測和診斷。 基于狀態(tài)的維修獲得主要是基于設(shè)備的狀態(tài)信息來預(yù)測設(shè)備的剩余壽命,以設(shè)定的優(yōu)化準(zhǔn)則為目標(biāo)對設(shè)備做出維修決策,即判斷設(shè)備是否需要進行預(yù)防性維修,如果需要,何時進行維修最合適。這種維修方式的維修間隔期是不固定的,其最大的特點是根據(jù)每個設(shè)備具體的狀態(tài),在設(shè)備故障發(fā)生前提早進行維修。對于設(shè)備,基于狀態(tài)的維修可以降低維護維修費用、提高設(shè)備的可用性和任務(wù)成功率；通過減少維修,尤其是計劃外的維修次數(shù),縮短維修時間,提高設(shè)備運行效率；通過減少備品備件、維修人員等日常維護保障開支,降低維護和維修成本；通過狀態(tài)監(jiān)測,降低任務(wù)失敗的風(fēng)險,進一步提高任務(wù)的成功率,極大的提升了設(shè)備維護和維修水平。
[Abstract]:With the continuous expansion of the network scale, the network equipment such as routers, switches and other devices running in the network is increasing day by day, which can ensure the normal operation of the network, maintain the network equipment without failure, and be able to quickly after the failure. It is a great challenge for network maintenance and management to locate and troubleshoot the problem accurately. In order to overcome the shortcomings of traditional maintenance methods, with the continuous progress of condition monitoring and fault diagnosis technology, a new maintenance mode, the condition based maintenance (CBM).), has been gradually developed. The maintenance method synthetically uses various technical means to obtain the running state data of the equipment, and then uses the fault prediction and diagnosis technology to distinguish the running state of the equipment, and predicts its development trend and what kind of fault to diagnose. Through the condition monitoring to predict the upcoming failure, a reasonable maintenance strategy is worked out. Fault prediction technology is an important part of fault diagnosis technology. By analyzing the history and current fault eigenvalues, it can predict the future fault eigenvalues, and then predict the running state of the equipment in a certain period of time. To predict the possible faults of the equipment, and to judge the fault level of the equipment according to these characteristic values, to grasp the development trend of the equipment faults in advance, and to provide the basis for the early prevention and repair of the faults. It has important theoretical research value and engineering practical significance. In this paper, a fault prediction method based on neural network is proposed, and the fault prediction model of equipment running in network based on multi-state is constructed by introducing the state-based maintenance technology. According to the severity of the fault, the model divides the warning level into four layers. For different early warning levels, neural networks are constructed, which solve the problem of low precision of equipment fault prediction and improve the ability of fault prediction based on multi-state. Through collecting the characteristic information of the network equipment, the characteristic information sample set of the equipment is obtained, and the designed neural network is used to train the sample set, and the design structure of the neural network is further optimized. The fault prediction model based on neural network is established to predict and diagnose the fault of equipment. Condition-based maintenance is mainly based on the state information of the equipment to predict the remaining life of the equipment, and make maintenance decisions on the equipment with the set optimization criteria as the goal, that is, to judge whether the equipment needs preventive maintenance, if so, When maintenance is most appropriate. The maintenance interval of this kind of maintenance method is not fixed, and its biggest characteristic is that according to the specific condition of each equipment, the maintenance should be carried out early before the failure of the equipment. For the equipment, the condition based maintenance can reduce the maintenance cost, improve the availability of the equipment and the success rate of the task, reduce the number of maintenance, especially the unplanned maintenance times, shorten the maintenance time, and improve the efficiency of the equipment operation. Reducing maintenance and maintenance costs by reducing daily maintenance support expenses such as spare parts and maintenance personnel, reducing the risk of mission failure through condition monitoring, and further improving the success rate of the task, Greatly improved the equipment maintenance and maintenance level.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.05;TP183

【引證文獻】

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1 楊健華;;網(wǎng)絡(luò)環(huán)境大背景下的設(shè)備動態(tài)故障診斷與預(yù)測維修[J];西部廣播電視;2016年22期

2 姚仲敏;沈玉會;;基于GA-BP的移動通信設(shè)備故障診斷[J];計算機測量與控制;2015年10期

相關(guān)碩士學(xué)位論文 前3條

1 張錢龍;基于信息融合的設(shè)備故障預(yù)測研究[D];鄭州大學(xué);2016年

2 賈永青;多變天氣環(huán)境下消防給水設(shè)備智能巡檢系統(tǒng)研究[D];湘潭大學(xué);2015年

3 王振華;基于日志分析的網(wǎng)絡(luò)設(shè)備故障預(yù)測研究[D];重慶大學(xué);2015年

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本文編號：2177632